1 Effect of UPSTM-Based Decorrelation on Feature Discovery

1.0.1 Loading the libraries

library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(TH.data)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

1.1 Material and Methods

About Dataset Coronaviruses are a broad family of viruses that have been linked to illnesses ranging from the common cold to more serious conditions such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS) (SARS. In 2019, a new coronavirus (COVID-19) was discovered in Wuhan, China.

Sometimes, When PCR test resources are scarce and antigen test kits are inaccurate, clinicians look for alternate COVID-19 testing methods that can be completed in a day and handle thousands of samples. COVID-19 virus proteins should be lacking in normal people’s saliva. Some distinct proteins may be produced in response to COVID-19 infection and can be utilized as a signature to identify potentially infected people. Therefore, protein profiles in a patient’s saliva can indicate that he or she is infected with COVID-19.

Mass spectrometry is a method for determining the protein composition of a material. Saliva samples from hundreds of patients were studied in this dataset. So, a machine learning specialist is approached and charged with developing a machine learning model that can identify who COVID-19 infected since the PCR test cannot interpret the result completely.

1.2 The Data

https://www.kaggle.com/datasets/kerneler/saliva-testing-dataset?select=COVID-19_MS_dataset_train.csv


COVID_19_MS <- read.csv("~/GitHub/LatentBiomarkers/Data/COVID19_MS/COVID-19_MS_dataset_train.csv")

colnames(COVID_19_MS) <- str_replace_all(colnames(COVID_19_MS),"\\.","_")
COVID_19_MS$Person_ID <- NULL
sampleID <- unique(COVID_19_MS$Sample_ID)
spectraID <- colnames(COVID_19_MS)[str_detect(colnames(COVID_19_MS),"X")]

avgCOVID19 <- NULL
class <- COVID_19_MS[!duplicated(COVID_19_MS$Sample_ID),"PCR_result"]
for (id in sampleID)
{
  avgCOVID19 <- rbind(avgCOVID19,apply(COVID_19_MS[COVID_19_MS$Sample_ID %in% id,spectraID],2,mean))
}

avgCOVID19 <- as.data.frame(avgCOVID19)
rownames(avgCOVID19) <- sampleID
avgCOVID19$class <- 1*(class=="pos")

pander::pander(table(avgCOVID19$class))
0 1
37 109

1.2.0.1 Standarize the names for the reporting

studyName <- "COVID19_MS"
dataframe <- avgCOVID19
outcome <- "class"
thro <- 0.80
TopVariables <- 10
cexheat = 0.25

1.3 Generaring the report

1.3.1 Libraries

Some libraries

library(psych)
library(whitening)
library("vioplot")
library("rpart")

1.3.2 Data specs

pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
rows col
146 2715 
pander::pander(table(dataframe[,outcome]))
0 1
37 109 

varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]

largeSet <- length(varlist) > 1500

1.3.3 Scaling the data

Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns


  ### Some global cleaning
  sdiszero <- apply(dataframe,2,sd) > 1.0e-16
  dataframe <- dataframe[,sdiszero]

  varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
  tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
  dataframe <- dataframe[,tokeep]

  varlist <- colnames(dataframe)
  varlist <- varlist[varlist != outcome]
  
  iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples



dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData

1.4 The heatmap of the data

numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000


if (!largeSet)
{

  hm <- heatMaps(data=dataframeScaled[1:numsub,],
                 Outcome=outcome,
                 Scale=TRUE,
                 hCluster = "row",
                 xlab="Feature",
                 ylab="Sample",
                 srtCol=45,
                 srtRow=45,
                 cexCol=cexheat,
                 cexRow=cexheat
                 )
  par(op)
}

1.4.0.1 Correlation Matrix of the Data

The heat map of the data


if (!largeSet)
{

  par(cex=0.6,cex.main=0.85,cex.axis=0.7)
  #cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
  cormat <- cor(dataframe[,varlist],method="pearson")
  cormat[is.na(cormat)] <- 0
  gplots::heatmap.2(abs(cormat),
                    trace = "none",
  #                  scale = "row",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Original Correlation",
                    cexRow = cexheat,
                    cexCol = cexheat,
                     srtCol=45,
                     srtRow=45,
                    key.title=NA,
                    key.xlab="|Pearson Correlation|",
                    xlab="Feature", ylab="Feature")
  diag(cormat) <- 0
  print(max(abs(cormat)))
}

1.5 The decorrelation


DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#> 
#>  Included: 2328 , Uni p: 0.004316151 , Uncorrelated Base: 1297 , Outcome-Driven Size: 0 , Base Size: 1297 
#> 
#> 
 1 <R=0.994,thr=0.900,N=  115>, Top: 28( 1 )[ 1 : 28 Fa= 28 : 0.900 ]( 28 , 55 , 0 ),<|>Tot Used: 83 , Added: 55 , Zero Std: 0 , Max Cor: 0.975
#> 
 2 <R=0.975,thr=0.900,N=  115>, Top: 6( 5 )[ 1 : 6 Fa= 34 : 0.900 ]( 6 , 20 , 28 ),<|>Tot Used: 107 , Added: 20 , Zero Std: 0 , Max Cor: 0.949
#> 
 3 <R=0.949,thr=0.900,N=  115>, Top: 1( 1 )[ 1 : 1 Fa= 35 : 0.900 ]( 1 , 1 , 34 ),<|>Tot Used: 109 , Added: 1 , Zero Std: 0 , Max Cor: 0.900
#> 
 4 <R=0.900,thr=0.800,N=  238>, Top: 66( 1 )[ 1 : 66 Fa= 84 : 0.800 ]( 66 , 117 , 35 ),<|>Tot Used: 262 , Added: 117 , Zero Std: 0 , Max Cor: 0.907
#> 
 5 <R=0.907,thr=0.900,N=    2>, Top: 1( 1 )[ 1 : 1 Fa= 85 : 0.900 ]( 1 , 1 , 84 ),<|>Tot Used: 262 , Added: 1 , Zero Std: 0 , Max Cor: 0.885
#> 
 6 <R=0.885,thr=0.800,N=   30>, Top: 10( 1 )[ 1 : 10 Fa= 92 : 0.800 ]( 10 , 16 , 85 ),<|>Tot Used: 282 , Added: 16 , Zero Std: 0 , Max Cor: 0.813
#> 
 7 <R=0.813,thr=0.800,N=   30>, Top: 1( 1 )[ 1 : 1 Fa= 93 : 0.800 ]( 1 , 1 , 92 ),<|>Tot Used: 284 , Added: 1 , Zero Std: 0 , Max Cor: 0.800
#> 
 8 <R=0.800,thr=0.800,N=   30>
#> 
 [ 8 ], 0.7981248 Decor Dimension: 284 Nused: 284 . Cor to Base: 177 , ABase: 29 , Outcome Base: 0 
#> 
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]

pander::pander(sum(apply(dataframe[,varlist],2,var)))

23332

pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))

5390

pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))

0.132

pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))

0.241

1.5.1 The decorrelation matrix


if (!largeSet)
{

  par(cex=0.6,cex.main=0.85,cex.axis=0.7)
  
  UPSTM <- attr(DEdataframe,"UPSTM")
  
  gplots::heatmap.2(1.0*(abs(UPSTM)>0),
                    trace = "none",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Decorrelation matrix",
                    cexRow = cexheat,
                    cexCol = cexheat,
                   srtCol=45,
                   srtRow=45,
                    key.title=NA,
                    key.xlab="|Beta|>0",
                    xlab="Output Feature", ylab="Input Feature")
  
  par(op)
}

1.6 The heatmap of the decorrelated data

if (!largeSet)
{

  hm <- heatMaps(data=DEdataframe[1:numsub,],
                 Outcome=outcome,
                 Scale=TRUE,
                 hCluster = "row",
                 cexRow = cexheat,
                 cexCol = cexheat,
                 srtCol=45,
                 srtRow=45,
                 xlab="Feature",
                 ylab="Sample")
  par(op)
}

1.7 The correlation matrix after decorrelation

if (!largeSet)
{

  cormat <- cor(DEdataframe[,varlistc],method="pearson")
  cormat[is.na(cormat)] <- 0
  
  gplots::heatmap.2(abs(cormat),
                    trace = "none",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Correlation after IDeA",
                    cexRow = cexheat,
                    cexCol = cexheat,
                     srtCol=45,
                     srtRow=45,
                    key.title=NA,
                    key.xlab="|Pearson Correlation|",
                    xlab="Feature", ylab="Feature")
  
  par(op)
  diag(cormat) <- 0
  print(max(abs(cormat)))
}

1.8 U-MAP Visualization of features

1.8.1 The UMAP based on LASSO on Raw Data


if (nrow(dataframe) < 1000)
{
  classes <- unique(dataframe[1:numsub,outcome])
  raincolors <- rainbow(length(classes))
  names(raincolors) <- classes
  datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
  plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
  text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}

1.8.2 The decorralted UMAP

if (nrow(dataframe) < 1000)
{

  datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
  plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
  text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}

1.9 Univariate Analysis

1.9.1 Univariate



univarRAW <- uniRankVar(varlist,
               paste(outcome,"~1"),
               outcome,
               dataframe,
               rankingTest="AUC")

100 : X297_73 200 : X382_77 300 : X479_57 400 : X629_81 500 : X796_11
600 : X969_8 700 : X1173_74 800 : X1364_32 900 : X1599_26 1000 : X1848_42
1100 : X2201_1 1200 : X2498_63 1300 : X2860_34 1400 : X3257_2 1500 : X3669_6
1600 : X4208_08 1700 : X4836_61 1800 : X5379_65 1900 : X6237_78 2000 : X7115_25
2100 : X8025_19 2200 : X9106_13 2300 : X10504_79 2400 : X11887_21 2500 : X13358_42
2600 : X14760_46




univarDe <- uniRankVar(varlistc,
               paste(outcome,"~1"),
               outcome,
               DEdataframe,
               rankingTest="AUC",
               )

100 : X297_73 200 : X382_77 300 : X479_57 400 : La_X629_81 500 : X796_11
600 : La_X969_8 700 : X1173_74 800 : X1364_32 900 : X1599_26 1000 : X1848_42
1100 : X2201_1 1200 : X2498_63 1300 : X2860_34 1400 : X3257_2 1500 : X3669_6
1600 : X4208_08 1700 : X4836_61 1800 : X5379_65 1900 : La_X6237_78 2000 : X7115_25
2100 : X8025_19 2200 : X9106_13 2300 : X10504_79 2400 : X11887_21 2500 : X13358_42
2600 : X14760_46

1.9.2 Final Table


univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")

##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
  caseMean caseStd controlMean controlStd controlKSP ROCAUC
X301_02 1.0971 1.288 5.780 4.428 0.37901 0.901
X1714_44 0.9653 1.354 2.775 2.143 0.16574 0.876
X1752_66 0.4318 0.300 1.092 0.570 0.52591 0.868
X1539_08 0.4536 0.386 1.205 0.790 0.08336 0.866
X900_35 0.7958 0.696 2.402 2.230 0.03660 0.841
X440_87 1.8551 1.815 4.788 3.289 0.14184 0.839
X1522_71 0.5368 0.438 1.262 0.787 0.54259 0.836
X3325_02 39.9174 33.638 5.890 11.627 0.00130 0.833
X3428_53 0.0818 0.313 0.566 1.047 0.00325 0.829
X256_5 2.4015 2.773 8.994 9.942 0.00576 0.824 


topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]


pander::pander(finalTable)
  caseMean caseStd controlMean controlStd controlKSP ROCAUC
X301_02 1.0971 1.288 5.780 4.428 0.37901 0.901
X1714_44 0.9653 1.354 2.775 2.143 0.16574 0.876
X1752_66 0.4318 0.300 1.092 0.570 0.52591 0.868
X1539_08 0.4536 0.386 1.205 0.790 0.08336 0.866
X900_35 0.7958 0.696 2.402 2.230 0.03660 0.841
X440_87 1.8551 1.815 4.788 3.289 0.14184 0.839
X1522_71 0.5368 0.438 1.262 0.787 0.54259 0.836
X3428_53 0.0818 0.313 0.566 1.047 0.00325 0.829
X256_5 2.4015 2.773 8.994 9.942 0.00576 0.824
X2870_03 0.6581 0.878 1.560 1.069 0.75629 0.821
La_X3679_57 0.0881 0.213 0.261 0.213 0.25991 0.785
La_X5722_3 -0.1910 0.186 0.121 0.491 0.01869 0.770
La_X758_77 0.1593 0.367 -0.235 0.576 0.33138 0.748
La_X263_75 6.7179 13.119 -8.218 17.344 0.77781 0.732
La_X818_6 -1.0304 2.015 0.542 3.338 0.32543 0.729 

dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")


pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
mean total fraction
2.03 204 0.0769 

theCharformulas <- attr(dc,"LatentCharFormulas")


finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])


orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]

Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")

finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
  DecorFormula caseMean caseStd controlMean controlStd controlKSP ROCAUC RAWAUC fscores
X301_02 NA 1.0971 1.288 5.780 4.428 0.37901 0.901 0.901 NA
X301_021 NA 1.0971 1.288 5.780 4.428 0.37901 0.901 NA NA
X1714_44 NA 0.9653 1.354 2.775 2.143 0.16574 0.876 0.876 NA
X1714_441 NA 0.9653 1.354 2.775 2.143 0.16574 0.876 NA NA
X1752_66 NA 0.4318 0.300 1.092 0.570 0.52591 0.868 0.868 NA
X1752_661 NA 0.4318 0.300 1.092 0.570 0.52591 0.868 NA NA
X1539_08 NA 0.4536 0.386 1.205 0.790 0.08336 0.866 0.866 NA
X1539_081 NA 0.4536 0.386 1.205 0.790 0.08336 0.866 NA NA
X900_35 NA 0.7958 0.696 2.402 2.230 0.03660 0.841 0.841 NA
X900_351 NA 0.7958 0.696 2.402 2.230 0.03660 0.841 NA NA
X440_87 NA 1.8551 1.815 4.788 3.289 0.14184 0.839 0.839 NA
X440_871 NA 1.8551 1.815 4.788 3.289 0.14184 0.839 NA NA
X1522_71 NA 0.5368 0.438 1.262 0.787 0.54259 0.836 0.836 NA
X1522_711 NA 0.5368 0.438 1.262 0.787 0.54259 0.836 NA NA
X3325_02 NA 39.9174 33.638 5.890 11.627 0.00130 0.833 0.833 NA
X3428_53 NA 0.0818 0.313 0.566 1.047 0.00325 0.829 0.829 NA
X3428_531 NA 0.0818 0.313 0.566 1.047 0.00325 0.829 NA NA
X256_5 NA 2.4015 2.773 8.994 9.942 0.00576 0.824 0.824 3
X256_51 NA 2.4015 2.773 8.994 9.942 0.00576 0.824 NA NA
X2870_03 NA 0.6581 0.878 1.560 1.069 0.75629 0.821 0.821 NA
La_X3679_57 + X3679_57 - (2.398)X7409_15 0.0881 0.213 0.261 0.213 0.25991 0.785 0.801 -1
La_X5722_3 - (0.870)X2962_99 + X5722_3 -0.1910 0.186 0.121 0.491 0.01869 0.770 0.662 -1
La_X758_77 - (0.332)X570_46 + X758_77 0.1593 0.367 -0.235 0.576 0.33138 0.748 0.585 -1
La_X263_75 + X263_75 - (0.593)X281_96 6.7179 13.119 -8.218 17.344 0.77781 0.732 0.565 -1
La_X818_6 - (1.127)X547_98 + (0.996)X570_46 + X818_6 -1.0304 2.015 0.542 3.338 0.32543 0.729 0.586 1

1.10 Comparing IDeA vs PCA vs EFA

1.10.1 PCA

featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE)   #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous]) 
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)

#pander::pander(pc$rotation)


PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])


  gplots::heatmap.2(abs(PCACor),
                    trace = "none",
  #                  scale = "row",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "PCA Correlation",
                    cexRow = 0.5,
                    cexCol = 0.5,
                     srtCol=45,
                     srtRow= -45,
                    key.title=NA,
                    key.xlab="Pearson Correlation",
                    xlab="Feature", ylab="Feature")

1.10.2 EFA


EFAdataframe <- dataframeScaled

if (length(iscontinous) < 2000)
{
  topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
  if (topred < 2) topred <- 2
  
  uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE)  # EFA analysis
  predEFA <- predict(uls,dataframeScaled[,iscontinous])
  EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
  colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous]) 


  
  EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
  
  
    gplots::heatmap.2(abs(EFACor),
                      trace = "none",
    #                  scale = "row",
                      mar = c(5,5),
                      col=rev(heat.colors(5)),
                      main = "EFA Correlation",
                      cexRow = 0.5,
                      cexCol = 0.5,
                       srtCol=45,
                       srtRow= -45,
                      key.title=NA,
                      key.xlab="Pearson Correlation",
                      xlab="Feature", ylab="Feature")
}

1.11 Effect on CAR modeling

par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")

  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(rawmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
  }


pander::pander(table(dataframe[,outcome],pr))
  0 1
0 31 6
1 4 105 
pander::pander(ptab$detail[c(5,3,4,6),])
  statistic est lower upper
5 diag.ac 0.932 0.878 0.967
3 se 0.963 0.909 0.990
4 sp 0.838 0.680 0.938
6 diag.or 135.625 35.975 511.303 

par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")

  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(IDeAmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
  }

pander::pander(table(DEdataframe[,outcome],pr))
  0 1
0 31 6
1 3 106 
pander::pander(ptab$detail[c(5,3,4,6),])
  statistic est lower upper
5 diag.ac 0.938 0.886 0.971
3 se 0.972 0.922 0.994
4 sp 0.838 0.680 0.938
6 diag.or 182.556 43.143 772.462 

par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
  plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
  text(PCAmodel, use.n = TRUE,cex=0.75)
  ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}

pander::pander(table(PCAdataframe[,outcome],pr))
  0 1
0 33 4
1 11 98 
pander::pander(ptab$detail[c(5,3,4,6),])
  statistic est lower upper
5 diag.ac 0.897 0.836 0.941
3 se 0.899 0.827 0.949
4 sp 0.892 0.746 0.970
6 diag.or 73.500 21.908 246.593 


par(op)

1.11.1 EFA


  EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
  EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
  pr <- predict(EFAmodel,EFAdataframe,type = "class")
  
  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(EFAmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
  }


  pander::pander(table(EFAdataframe[,outcome],pr))
  0 1
0 31 6
1 4 105 
  pander::pander(ptab$detail[c(5,3,4,6),])
  statistic est lower upper
5 diag.ac 0.932 0.878 0.967
3 se 0.963 0.909 0.990
4 sp 0.838 0.680 0.938
6 diag.or 135.625 35.975 511.303 
  par(op)